Aggregating Incomplete and Noisy Rankings

不完全信息下的多智能体评估

Multiagent Evaluation under Incomplete Information

本文针对不完全信息下多智能体评估问题，提出基于自适应算法的排名方法，克服Elo和α-Rank的局限，处理噪声结果，并提供样本复杂度保证，在伯努利博弈、足球元博弈和库恩扑克中验证有效性。

Label Ranking through Nonparametric Regression

Label Ranking (LR) corresponds to the problem of learning a hypothesis that maps features to rankings over a finite set of labels. We adopt a nonparametric regression approach to LR and obtain theoretical performance guarantees for this fundamental practical problem. We introduce a generative model for Label Ranking, in noiseless and noisy nonparametric regression settings, and provide sample complexity bounds for learning algorithms in both cases. In the noiseless setting, we study the LR problem with full rankings and provide computationally efficient algorithms using decision trees and rand…

迭代特征排除排名用于深度表格学习

Iterative Feature Exclusion Ranking for Deep Tabular Learning

本文提出迭代特征排除模块，通过迭代排除特征并计算注意力分数，提升表格数据中特征重要性排名和分类性能。

Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening

We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete one, which we call the coarsening process. To this end, we propose the concept of rank-dependent coarsening, which assumes that incomplete rankings are produced by projecting a full ranking to a random subset of ranks. For a concrete instantiation of our model, in which full rankin…

Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics

In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by $U$-statistics of degree $d\geq 1$, i.e. functionals of the training data with low variance that take the form of averages over $k$-tuples. From a computational perspective, the calculation of such statistics is highly expensive even for a moderate sample size $n$, as it requires averaging $O(n^d)$ terms. This makes learning procedures relying on the optimization of such data functionals hardly feasible in practice. It is the major goal of this paper…

Spectral Ranking with Covariates

We consider spectral approaches to the problem of ranking n players given their incomplete and noisy pairwise comparisons, but revisit this classical problem in light of player covariate information. We propose three spectral ranking methods that incorporate player covariates and are based on seriation, low-rank structure assumption and canonical correlation, respectively. Extensive numerical simulations on both synthetic and real-world data sets demonstrated that our proposed methods compare favorably to existing state-of-the-art covariate-based ranking algorithms.